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Advanced semiconductor fabrication process control using dual filter exponentially weighted moving average

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dc.contributor.authorKo, Hyo-Heon-
dc.contributor.authorKim, Jihyun-
dc.contributor.authorPark, Sang-Hoon-
dc.contributor.authorBaek, Jun-Geol-
dc.contributor.authorKim, Sung-Shick-
dc.date.accessioned2021-09-06T19:07:05Z-
dc.date.available2021-09-06T19:07:05Z-
dc.date.created2021-06-18-
dc.date.issued2012-06-
dc.identifier.issn0956-5515-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/108243-
dc.description.abstractSemiconductor industry needs to meet high standards to ensure survival and success in the 21st century. Rising expectations from the customers are demanding the semiconductor industry to manufacture products with both accuracy and precision. To comply with the strict demands, an effective control method for semiconductor manufacturing is introduced. The process environment is afflicted by process disturbances. Different characteristics of the process disturbances require the control method to be able to respond accordingly. This study utilizes two separate exponentially weighted moving average (EWMA) filters simultaneously to improve the performance of the control method. By utilizing dual filters, the influence of the white noise is reduced and the accurate process control is made possible. The proposed methodology is evaluated through simulation in comparison with two other control methods.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherSPRINGER-
dc.subjectTO-RUN CONTROL-
dc.subjectNEURAL-NETWORKS-
dc.subjectOVERLAY-
dc.titleAdvanced semiconductor fabrication process control using dual filter exponentially weighted moving average-
dc.typeArticle-
dc.contributor.affiliatedAuthorBaek, Jun-Geol-
dc.contributor.affiliatedAuthorKim, Sung-Shick-
dc.identifier.doi10.1007/s10845-010-0383-6-
dc.identifier.scopusid2-s2.0-84862302827-
dc.identifier.wosid000304160600008-
dc.identifier.bibliographicCitationJOURNAL OF INTELLIGENT MANUFACTURING, v.23, no.3, pp.443 - 455-
dc.relation.isPartOfJOURNAL OF INTELLIGENT MANUFACTURING-
dc.citation.titleJOURNAL OF INTELLIGENT MANUFACTURING-
dc.citation.volume23-
dc.citation.number3-
dc.citation.startPage443-
dc.citation.endPage455-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryEngineering, Manufacturing-
dc.subject.keywordPlusTO-RUN CONTROL-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusOVERLAY-
dc.subject.keywordAuthorSemiconductor fabrication process-
dc.subject.keywordAuthorProcess control-
dc.subject.keywordAuthorRun-to-run-
dc.subject.keywordAuthorEWMA-
dc.subject.keywordAuthorDual filter EWMA-
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